Fix TargetRegistry de/serialization and fix FixedPointIteratorPass constructor (#24867)

Fixed reproducer creation: The target registry is serialized as
`{target_registry=0}` in the reproducer file (for passes that contain
it, e.g. JitGlobalsPass). The deserialization when running a reproducer
thus fails, because it expects `{target_registry=global}`.


What changed?
Enabled -print-pipeline-passes
Fix TargetRegistry de/serialization
Fix FixedPointIteratorPass constructor

More detailed explanation of the fixes
- see
https://github.com/iree-org/llvm-project/blob/e65959f1684bd2416e8aa2ce684b8c300c381902/mlir/include/mlir/Pass/PassOptions.h#L77-L85
- Making the conversion from TargetRegistryRef to bool explicit, so that
`<<` is no longer overloaded for TargetRegistryDef (otherwise an
implicit conversion would be inserted, and simply 0 or 1 printed),
leading to the wrong output in the reproducer file
- Add a `parser<TargetRegistryRef>::print` overload that is selected
instead, printing either "global" or "unknown"
- Adapting TargetRegistryRef parsing; always select the global registry,
but print a warning if an argument different from "global" is provided

---------

Signed-off-by: Jakob Knauer <knauer@roofline.ai>
Co-authored-by: Tobias Fuchs <fuchs@roofline.ai>
7 files changed
tree: e13c94e030bee44a696de323bb933d4660fb32c3
  1. .github/
  2. build_tools/
  3. compiler/
  4. docs/
  5. experimental/
  6. integrations/
  7. lib/
  8. llvm-external-projects/
  9. runtime/
  10. samples/
  11. tests/
  12. third_party/
  13. tools/
  14. .bazel_to_cmake.cfg.py
  15. .bazelignore
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  17. .bazelversion
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  20. .gitattributes
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  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. GOVERNANCE.md
  32. LICENSE
  33. MAINTAINERS.md
  34. MODULE.bazel
  35. README.md
  36. RELEASING.md
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.

See our website for project details, user guides, and instructions on building from source.

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DateTitleRecordingSlides
2025-06-10Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM)recordingslides
2025-05-17Introduction to GPU architecture and IREE's GPU CodeGen Pipelinerecordingslides
2025-02-12The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised)recordingslides
2024-10-01Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardwarerecording
2021-06-09IREE Runtime Design Tech Talkrecordingslides
2020-08-20IREE CodeGen (MLIR Open Design Meeting)recordingslides
2020-03-18Interactive HAL IR Walkthroughrecording
2020-01-31End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting)recordingslides

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IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.